Class activation mapping (CAMs) methods are commonly employed to perform WSOL with convolutional neural networks (CNNs) [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}. These methods allow producing a spatial activation map highlight features belonging to a particular class using features from the penultimate layer [13]} of a CNN. Strong activation of a map indicates potential presence of the corresponding class, allowing for object localization.
To improve the robustness of these maps, several techniques have been proposed to obtain maps from different layers by utilizing gradient information [14]}, [15]}, [16]}. Despite their popularity, CAM methods have limitations leading to inaccurate localizations. Activation maps tend to focus on small discriminant area of objects – common to different instance of an object belonging to a specific class. Current efforts in WSOL task are focusing on improving the CAMs by covering the full object.
